vRAP HARQ Prediction for Cloud RAN Decoding Fluctuations

Solution Overview

Problem

Virtualized radio access points (vRANs) face significant performance degradation due to resource contention in cloud infrastructure, leading to unpredictable computing fluctuations that are catastrophic for 4G/5G PHY pipelines, necessitating improved DU virtualization to maintain carrier-grade performance.

Innovation Solution

Implementing a Hybrid Automated Repeat Request (HARQ) prediction mechanism and congestion control to leverage extrinsic information from decoders for decodability inference, allowing data processing to continue while adapting data rates to computing capacity, thereby avoiding unnecessary retransmissions and optimizing resource allocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If virtualized DUs are deployed on cloud platforms to achieve flexibility and cost-efficiency, then resource utilization improves, but performance degradation occurs due to resource contention and unpredictable computing fluctuations

Engineering Contradiction:

ImproveflexibilityVSAvoidperformance predictability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system performs preliminary actions by predicting decodability outcomes before actual decoding completion. The HARQ prediction mechanism analyzes intermediate decoding states and extrinsic information in advance to forecast whether decoding will succeed, allowing the system to prepare appropriate responses proactively rather than reactively after performance degradation occurs

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring decoding progress and extrinsic information from the decoder. This feedback loop enables the HARQ prediction mechanism to assess decoding status in real-time and adjust predictions dynamically, allowing the virtualized DU to adapt to computing fluctuations while maintaining performance predictability

Inventive Principle:

Principle #23Feedback

2Ease of manufacture

If resource contention in cloud infrastructure is accepted to achieve higher flexibility, then cost-efficiency improves, but computing fluctuations cause catastrophic performance degradation for PHY pipelines

Engineering Contradiction:

Improvecost-efficiencyVSAvoidprocessing throughput

Core Design Contradiction:

Ease of manufactureVSProductivity

Solution Approach 1:

The system performs preliminary actions by predicting decodability outcomes before actual decoding completion. The HARQ prediction mechanism analyzes intermediate decoding states and extrinsic information in advance to forecast whether decoding will succeed, allowing the system to prepare appropriate responses proactively rather than reactively after performance degradation occurs

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system ensures continuity of useful action by allowing data processing to continue in parallel with HARQ prediction operations. The prediction mechanism does not interrupt the decoding pipeline but operates concurrently, maintaining continuous processing throughput while providing predictive insights to mitigate the impact of resource contention

Inventive Principle:

Principle #20Continuity of useful action

3Loss of time

If early stopping criteria are applied to reduce delay, then latency improves, but decoding accuracy may be compromised and spectrum efficiency decreases

Engineering Contradiction:

Improvedecoding delayVSAvoiddecoding accuracy

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The system implements feedback mechanisms by continuously monitoring decoding progress and extrinsic information from the decoder. This feedback loop enables the HARQ prediction mechanism to assess decoding status in real-time and adjust predictions dynamically, allowing the virtualized DU to adapt to computing fluctuations while maintaining performance predictability

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system applies parameter changes by utilizing extrinsic information metrics as dynamic parameters for prediction. The HARQ prediction mechanism monitors changes in extrinsic information magnitude and patterns, adjusting its predictions based on these parameter variations to accurately forecast decodability outcomes without relying on fixed early stopping criteria

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentEP4327460B1Virtualized radio access point, vrap, and method of operating the same

Publication Date: 2026.03.25 NEC CORP

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AI summary

The present invention relates to a virtualized radio access point, vRAP, as well as to a method of operating the same. With regard to enabling high-performing DU virtualization in order to maximize performance in cloud-based virtualized RANs, the vRAP comprises an encoder/decoder configured to encode/decode transport blocks, TBs, by using iterative codes such as turbo codes or LDPC codes that exchange extrinsic information in each decoding iteration; and a digital signal processor, DSP, pipeline configured to infer information about the decodability of the data of the TBs by exploiting the exchanged extrinsic information.